Summary of Unihr: Hierarchical Representation Learning For Unified Knowledge Graph Link Prediction, by Zhiqiang Liu et al.
UniHR: Hierarchical Representation Learning for Unified Knowledge Graph Link Prediction
by Zhiqiang Liu, Mingyang Chen, Yin Hua, Zhuo Chen, Ziqi Liu, Lei Liang, Huajun Chen, Wen Zhang
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Unified Hierarchical Representation learning framework, UniHR, is proposed for unified knowledge graph link prediction. The framework consists of two modules: HiDR and HiSL. HiDR unifies triple-based representations from different types of KGs, while HiSL incorporates message passing to enhance semantic information within individual facts and structural information between facts. Experimental results across 7 datasets demonstrate that UniHR outperforms baselines designed for one specific type of KG, indicating strong generalization capability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict relationships in knowledge graphs is being developed. This approach can work with different types of data and improve its performance by learning more about the structure of the data. The researchers tested their method on several datasets and found that it worked better than other methods designed for specific types of data. |
Keywords
» Artificial intelligence » Generalization » Knowledge graph » Representation learning
